PrivyReveal

Inspiration

The inspiration behind PrivyReveal came from a universal frustration: almost everyone clicks “I Agree” without reading or understanding the terms.

Privacy policies are: • long (30–45 minutes to read) • filled with legal jargon • intentionally complex • nearly impossible for everyday users to evaluate

I experienced this myself, and I’ve seen many people unknowingly consent to risky or harmful clauses. The gap between what users think they’re agreeing to and what’s actually written inspired us to create PrivyReveal — an AI tool built to restore transparency, comprehension, and digital autonomy.

What it does

PrivyReveal is an AI-powered policy analysis and explanation tool that helps users instantly understand any Terms of Service or Privacy Policy.

It provides:

Clear, human-readable summaries

Automatically simplifies complex legal clauses.

Risk detection

Highlights risky, harmful, or concerning statements.

Topic-based filtering

Lets users explore data collection, sharing, security, retention, cookies, dispute resolution, and more.

XAI Explainability

Shows how the AI made decisions using SHAP/LIME visual explanations.

Voice interaction

Users can ask questions about the document via text or voice, powered by Claude on Amazon Bedrock + Deepgram.

Evaluation transparency

Uses Galileo traces to show model behavior and build user trust.

The result: A fast, accessible, trustworthy way to understand what you’re really agreeing to.

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How we built it

PrivyReveal was developed using: • Python • Gradio for the interactive UI • Transformer models (RoBERTa/ToS;DR-based classification) • Deepgram for speech-to-text • ElevenLabs for text-to-speech • SHAP & LIME for explainability • Galileo for evaluation and trace analysis • Custom privacy ontology for hierarchical categorization

This allowed us to deliver a full-stack AI product within a single sprint.

Challenges we ran into

Building PrivyReveal came with several challenges:

Daytona environment compatibility

Model dependencies, Bedrock authentication, and SDK versions needed careful setup.

UI rendering issues

Coordinating tables, filters, charts, and voice components inside Gradio required multiple fixes.

XAI formatting

LIME charts and SHAP outputs needed custom patches to avoid overlapping text and improve readability.

Bedrock model errors

Switching between bearer-token authentication, region mismatches, and Converse vs. Invoke APIs caused repeated failures.

Despite this, we achieved a stable, polished system.

Accomplishments that we're proud of

A complete end-to-end AI tool built in a one-day sprint
• Successful integration of Claude, Deepgram, ElevenLabs, Galileo, Daytona
• A polished UI that makes privacy understandable and accessible
• Voice → AI → voice loop working seamlessly
• A system with real-world social impact potential
• Built-in evaluation and explainability for trustworthy AI
• A tool that genuinely empowers users to understand digital consent

What we learned

Throughout this build, we learned: • How to integrate LLMs through Amazon Bedrock’s Converse API • Real-world application of explainable AI techniques • How to build fast, reliable voice pipelines • Advanced Gradio UI design under constraints • Managing multi-agent toolchains and debugging complex systems • Designing AI tools for trust, transparency, and safety

What's next for PrivyReveal

Future plans for PrivyReveal include: • Browser extension for instant on-page analysis • Multi-language support • Comparative scoring across websites and apps • User persona–based risk alerts • Policy change detection • Enterprise compliance dashboards • Mobile app with conversational guidance

Our long-term vision is simple: Make digital consent honest, transparent, and user-centric.